Chemotherapy aided by opening of the blood-brain barrier with intra-arterial infusion of hyperosmolar mannitol improves the outcome in primary central nervous system lymphoma. Proper opening of the blood-brain barrier is crucial for the treatment, yet there are no means available for its real-time monitoring. The intact blood-brain barrier maintains a mV-level electrical potential difference between blood and brain tissue, giving rise to a measurable electrical signal at the scalp. Therefore, we used direct-current electroencephalography (DC-EEG) to characterize the spatiotemporal behavior of scalp-recorded slow electrical signals during blood-brain barrier opening. Nine anesthetized patients receiving chemotherapy were monitored continuously during 47 blood-brain barrier openings induced by carotid or vertebral artery mannitol infusion. Left or right carotid artery mannitol infusion generated a strongly lateralized DC-EEG response that began with a 2 min negative shift of up to 2000 μV followed by a positive shift lasting up to 20 min above the infused carotid artery territory, whereas contralateral responses were of opposite polarity. Vertebral artery mannitol infusion gave rise to a minimally lateralized and more uniformly distributed slow negative response with a posterior-frontal gradient. Simultaneously performed near-infrared spectroscopy detected a multiphasic response beginning with mannitol-bolus induced dilution of blood and ending in a prolonged increase in the oxy/deoxyhemoglobin ratio. The pronounced DC-EEG shifts are readily accounted for by opening and sealing of the blood-brain barrier. These data show that DC-EEG is a promising real-time monitoring tool for blood-brain barrier disruption augmented drug delivery.

Background: A high dose of opioids associated with a low dose of propofol has become a popular anesthetic technique. However, the influence of opioids on the electroencephalographic phenomenon related to induction of anesthesia and, thereby, on the quantitative parameters used in the depth-of-anesthesia estimation is not well known. Methods: Twenty-seven patients were divided into three groups to receive saline, low-dose remifentanil (7.5 microg x kg x h) or high-dose remifentanil (30 microg x kg x h) during induction of anesthesia with propofol (30 mg . kg . h). Electroencephalogram was recorded from Fz electrode, and its time-frequency properties in the patient groups were analyzed from the induction of anesthesia to the occurrence of burst suppression pattern. The group differences in 14 quantitative spectral parameters used in the depth-of-anesthesia estimation were examined as well. Results: The time-frequency properties of electroencephalogram were different between groups. The high-frequency (greater than 14 Hz) activity during light anesthesia was decreased in remifentanil groups; whereas, increased activity in extended alpha band (7-14 Hz) and decreased activity in delta band (0.5-4 Hz) was observed during deep anesthesia. This resulted in statistically significant changes in all 14 quantitative parameters. Conclusions: The effect of remifentanil on the spectrum and quantitative parameters of electroencephalogram is significant and strongly dependent on the level of anesthesia. Coadministration of opioids therefore challenges the reliability of the spectral properties of electroencephalogram in the depth-of-anesthesia estimation by using a frontal montage. Furthermore, the finding has implications for design of opioid coadministration studies.

Background: Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative information from high-dimensional data collected from multiple sources. The available fusion algorithms with their complex structure are poorly adopted to the computationally constrained environment which requires integrating information directly at the source. As a result, more simple low-level fusion methods are needed. Objective: In the absence of a data combining process, the cost of directly applying high-dimensional raw data to a deep classifier would be computationally expensive with regard to the response time, energy consumption, and memory requirement. Taking this into account, we aimed to develop a data fusion technique in a computationally efficient way to achieve a more comprehensive insight of human activity dynamics in a lower dimension. The major objective was considering statistical dependency of multisensory data and exploring intermodality correlation patterns for different activities. Methods: In this technique, the information in time (regardless of the number of sources) is transformed into a 2D space that facilitates classification of eating episodes from others. This is based on a hypothesis that data captured by various sensors are statistically associated with each other and the covariance matrix of all these signals has a unique distribution correlated with each activity which can be encoded on a contour representation. These representations are then used as input of a deep model to learn specific patterns associated with specific activity. Results: In order to show the generalizability of the proposed fusion algorithm, 2 different scenarios were taken into account. These scenarios were different in terms of temporal segment size, type of activity, wearable device, subjects, and deep learning architecture. The first scenario used a data set in which a single participant performed a limited number of activities while wearing the Empatica E4 wristband. In the second scenario, a data set related to the activities of daily living was used where 10 different participants wore inertial measurement units while performing a more complex set of activities. The precision metric obtained from leave-one-subject-out cross-validation for the second scenario reached 0.803. The impact of missing data on performance degradation was also evaluated. Conclusions: To conclude, the proposed fusion technique provides the possibility of embedding joint variability information over different modalities in just a single 2D representation which results in obtaining a more global view of different aspects of daily human activities at hand, and yet preserving the desired performance level in activity recognition.

Increasing concentrations of anesthetics in the blood induce a continuum of neurophysiological changes, which reflect on the electroencephalogram (EEG). EEG-based depth of anesthesia assessment requires that the signal samples are correctly associated with the neurophysiological changes occurring at different anesthetic levels. A novel method is presented to estimate the phase of the continuum using the feature data extracted from EEG. The feature data calculated from EEG sequences corresponding to continuously deepening anesthesia are considered to form a one-dimensional nonlinear manifold in the multidimensional feature space. Utilizing a recently proposed algorithm, Isomap, the dimensionality of the feature data is reduced to achieve a one-dimensional embedding representing this manifold and thereby the continuum of neurophysiological changes during induction of anesthesia. The Isomap-based estimation is validated with data recorded from nine patients during induction of propofol anesthesia. The proposed method provides a novel approach to assess neurophysiological changes during anesthesia and offers potential for the development of more advanced systems for the depth of anesthesia monitoring.